---
title: "Example: Faithfulness | Evals | Kastrax Docs"
description: Example of using the Faithfulness metric to evaluate how factually accurate responses are compared to context.
---

import { GithubLink } from "@/components/github-link";

# Faithfulness ✅

This example demonstrates how to use Kastrax's Faithfulness metric to evaluate how factually accurate responses are compared to the provided context.

## Overview ✅

The example shows how to:

1. Configure the Faithfulness metric
2. Evaluate factual accuracy
3. Analyze faithfulness scores
4. Handle different accuracy levels

## Setup ✅

### Environment Setup

Make sure to set up your environment variables:

```bash filename=".env"
OPENAI_API_KEY=your_api_key_here
```

### Dependencies

Import the necessary dependencies:

```typescript copy showLineNumbers filename="src/index.ts"
import { openai } from '@ai-sdk/openai';
import { FaithfulnessMetric } from '@kastrax/evals/llm';
```

## Example Usage ✅

### High Faithfulness Example

Evaluate a response where all claims are supported by context:

```typescript copy showLineNumbers{5} filename="src/index.ts"
const context1 = [
  'The Tesla Model 3 was launched in 2017.',
  'It has a range of up to 358 miles.',
  'The base model accelerates 0-60 mph in 5.8 seconds.',
];

const metric1 = new FaithfulnessMetric(openai('gpt-4o-mini'), {
  context: context1,
});

const query1 = 'Tell me about the Tesla Model 3.';
const response1 = 'The Tesla Model 3 was introduced in 2017. It can travel up to 358 miles on a single charge and the base version goes from 0 to 60 mph in 5.8 seconds.';

console.log('Example 1 - High Faithfulness:');
console.log('Context:', context1);
console.log('Query:', query1);
console.log('Response:', response1);

const result1 = await metric1.measure(query1, response1);
console.log('Metric Result:', {
  score: result1.score,
  reason: result1.info.reason,
});
// Example Output:
// Metric Result: { score: 1, reason: 'All claims are supported by the context.' }
```

### Mixed Faithfulness Example

Evaluate a response with some unsupported claims:

```typescript copy showLineNumbers{31} filename="src/index.ts"
const context2 = [
  'Python was created by Guido van Rossum.',
  'The first version was released in 1991.',
  'Python emphasizes code readability.',
];

const metric2 = new FaithfulnessMetric(openai('gpt-4o-mini'), {
  context: context2,
});

const query2 = 'What can you tell me about Python?';
const response2 = 'Python was created by Guido van Rossum and released in 1991. It is the most popular programming language today and is used by millions of developers worldwide.';

console.log('Example 2 - Mixed Faithfulness:');
console.log('Context:', context2);
console.log('Query:', query2);
console.log('Response:', response2);

const result2 = await metric2.measure(query2, response2);
console.log('Metric Result:', {
  score: result2.score,
  reason: result2.info.reason,
});
// Example Output:
// Metric Result: { score: 0.5, reason: 'Only half of the claims are supported by the context.' }
```

### Low Faithfulness Example

Evaluate a response that contradicts context:

```typescript copy showLineNumbers{57} filename="src/index.ts"
const context3 = [
  'Mars is the fourth planet from the Sun.',
  'It has a thin atmosphere of mostly carbon dioxide.',
  'Two small moons orbit Mars: Phobos and Deimos.',
];

const metric3 = new FaithfulnessMetric(openai('gpt-4o-mini'), {
  context: context3,
});

const query3 = 'What do we know about Mars?';
const response3 = 'Mars is the third planet from the Sun. It has a thick atmosphere rich in oxygen and nitrogen, and is orbited by three large moons.';

console.log('Example 3 - Low Faithfulness:');
console.log('Context:', context3);
console.log('Query:', query3);
console.log('Response:', response3);

const result3 = await metric3.measure(query3, response3);
console.log('Metric Result:', {
  score: result3.score,
  reason: result3.info.reason,
});
// Example Output:
// Metric Result: { score: 0, reason: 'The response contradicts the context.' }
```

## Understanding the Results ✅

The metric provides:

1. A faithfulness score between 0 and 1:
   - 1.0: Perfect faithfulness - all claims supported by context
   - 0.7-0.9: High faithfulness - most claims supported
   - 0.4-0.6: Mixed faithfulness - some claims unsupported
   - 0.1-0.3: Low faithfulness - most claims unsupported
   - 0.0: No faithfulness - claims contradict context

2. Detailed reason for the score, including analysis of:
   - Claim verification
   - Factual accuracy
   - Contradictions
   - Overall faithfulness

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<GithubLink
  link={
    "https://github.com/kastrax-ai/kastrax/blob/main/examples/basics/evals/faithfulness"
  }
/>
